Tremendous studies show that deep learning methods have potential for providing accurate and quantitative assessment of COVID-19 infection in CT scans if hundreds of well-labeled training cases are available. However, manual delineation of lung and infection is time-consuming and labor-intensive. Thus, we set up this benchmark to explore annotation-efficient methods for COVID-19 CT scans segmentation. In particular, we focus on learning to segment left lung, right lung and infection using
pure but limited COVID-19 CT scans;
existing labeled lung CT dataset from other non-COVID-19 lung diseases;
heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.
Ultimate goal: training a model on limited data that can generalize on infinite data!
@article{MP-COVID-19-SegBenchmark,
title={Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation},
author = {Ma, Jun and Wang, Yixin and An, Xingle and Ge, Cheng and Yu, Ziqi and Chen, Jianan and Zhu, Qiongjie and Dong, Guoqiang and He, Jian and He, Zhiqiang and Cao, Tianjia and Zhu, Yuntao and Nie, Ziwei and Yang, Xiaoping},
journal = {Medical Physics},
volume = {48},
number = {3},
pages = {1197-1210},
doi = {https://doi.org/10.1002/mp.14676},
year = {2021}
}
Download Dataset | Description | License |
---|---|---|
StructSeg 2019 | 50 lung CT scans; Annotations include left lung, right lung, spinal cord, esophagus, heart, trachea and gross target volume of lung cancer. | Hold by the challenge organizers |
NSCLC | 402 lung CT scans; Annotations include left lung, right lung and pleural effusion (78 cases). | CC BY-NC |
MSD Lung Tumor | 63 lung CT scans; Annotations include lung cancer. | CC BY-SA |
COVID-19-CT-Seg | 20 lung CT scans; Annotations include left lung, right lung and infections. | CC BY-NC-SA |
MosMed | 50 labelled COVID-19 CT scans; Annotations include infections. | CC BY-NC-ND |
This task is based on the COVID-19-CT-Seg dataset with 20 cases. Three subtasks are to segment lung, infection or both of them. For each task, 5-fold cross-validation results should be reported. It should be noted that each fold only has 4 training cases, and remained 16 cases are used for testing. In other words, this is a few-shot or zero-shot segmentation task. Dataset split file and quantitative results of U-Net baseline are presented in Task1 folder.
Subtask | ||
---|---|---|
Lung | 5-fold cross validation 4 cases (20% for training) 16 cases (80% for testing) |
MosMed(50) |
Infection | ||
Lung and infection |
This task is to segment lung and infection in COVID-19 CT scans. The main difficulty is that the training set and testing set differ in data distribution. Although all the datasets are lung CT, they vary in lesion types (i.e., cancer, pleural effusion, and COVID-19), patient cohorts and imaging scanners.
It should be noted that labeled COVID-19 CT scans are not allowed to be used during training. The following table presents the details of training, validation, and testing set. Name (Num.) denotes the dataset name and the number of cases in this dataset, e.g., StructSeg Lung (40) denotes that 40 cases in StructSeg dataset are used for training.
Dataset split file and quantitative results of U-Net baseline are presented in Task2 folder.
Subtask | Training | In-domain Testing | (Unseen)Testing 1 | (Unseen)Testing 2 |
---|---|---|---|---|
Lung | StructSeg Lung (40) NSCLC Lung (322) |
StructSeg Lung (10) NSCLC Lung (80) |
COVID-19-CT-Seg Lung (20) |
- |
Infection | MSD Lung Tumor (51) StructSeg Gross Target (40) NSCLC Plcural Effusion (62) |
MSD Lung Tumor (12) StructSeg Gross Target (10) NSCLC Plcural Effusion (16) |
COVID-19-CT-Seg Infection(20) |
MosMed(50) |
This task is also to segment lung and infection in COVID-19 CT scans, but a limited labeled COVID-19 CT scans are allowed to be used during training. For each subtask, 5-fold cross-validation results should be reported.
Dataset split file and quantitative results of U-Net baseline will be presented in Task3 folder.
Subtask | |||||
---|---|---|---|---|---|
Lung | NSCLC Lung (322) |
NSCLC Lung (80) |
- | ||
Infection | StructSeg Gross Target (40) NSCLC Plcural Effusion (62) |
StructSeg Gross Target (10) NSCLC Plcural Effusion (16) |
MosMed(50) |
Baidu Net Disk mirror (pw: t5mj)
3DU-Net | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Task1-Separate | 87.9±9.3 | |||||||||
Task1-Union | ||||||||||
Task2-MSD | - | - | - | - | ||||||
Task2-StructSeg | 95.5±7.2 | 6.0±12.7 | 5.5±10.7 | |||||||
Task2-NSCLC | 0.4±0.9 | 3.7±4.8 | ||||||||
Task3-MSD | 96.5±2.8 | 87.9±7.9 | 96.9±2.2 | 88.5±7.1 | ||||||
Task3-StructSeg | 97.3±2.1 | 90.6±6.2 | 97.7±2.1 | 91.4±6.1 | ||||||
Task3-NSCLC | 93.5±5.4 | 94.0±5.3 | ||||||||
2DU-Net | ||||||||||
Task1-Separate | 95.6±7.4 | |||||||||
Task1-Union | ||||||||||
Task2-MSD | - | - | - | - | ||||||
Task2-StructSeg | 45.3±46.7 | 0.2±0.8 | 0.6±1.6 | |||||||
Task2-NSCLC | 1.2±2.9 | 7.3±9.7 | ||||||||
Task3-MSD | 96.9±4.9 | 89.8±9.1 | 97.1±4.9 | 89.8±9.1 | ||||||
Task3-StructSeg | 96.3±7.6 | 88.7±10.8 | 96.7±7.0 | 89.0±11.6 | ||||||
Task3-NSCLC | 92.5±17.3 | 93.3±15.9 |
Step 1. Install the nnU-Net following the official guidance.
Step 2. Download the 3D or 2D trained models and put them into your model folder.
Step 3. Run the inference code.
Due to the license limitation, we can not directly share this dataset, pleanse download it from the official homepage.
[x] Provide pretrained 3D U-Net models by 5.6.
[x] Provide pretrained 2D U-Net models by 5.31.
[x] Provide lung annotations of MSD dataset by 6.30.
We thank all the organizers of MICCAI 2018 Medical Segmentation Decathlon, MICCAI 2019 Automatic Structure Segmentation for Radiotherapy Planning Challenge, the Coronacases Initiative and Radiopaedia for the publicly available lung CT dataset. We also thank Joseph Paul Cohen for providing convenient download link of 20 COVID-19 CT scans. We also thank all the contributor of NSCLC and COVID-19-Seg-CT dataset for providing annotations of lung, pleural effusion and COVID-19 infection. We also thank the organizers of TMI Special Issue on Annotation-Efficient Deep Learning for Medical Imaging because we get lots of insights from the call for papers when designing these segmentation tasks. We also thank the contributors of these great COVID-19 related resources: COVID19_imaging_AI_paper_list and MedSeg. Last but not least, we thank Chen Chen, Xin Yang, and Yao Zhang for their important feedback on this benchmark.